Additionally, we show that predicted probabilities of adversarially trained models are significantly better calibrated on common corruptions.
We believe that calibration is another important aspect of the model's trustworthiness, which is particularly important in the presence of out-of-distribution data such as corrupted images.
In Fig. REF , we plot the expected calibration error (ECE) [1]} on CIFAR-10-C for models trained with different \(\operatorname{\mathbf {\ell }}_\infty \) -radii.
We observe that the ECE—both with and without temperature rescaling (see App.  for details)—follows a decreasing trend over \(\operatorname{\mathbf {\ell }}_\infty \) -radii which is expected since a classifier that predicts uniform probabilities over classes is perfectly calibrated.
In particular, the most accurate model trained with \(\operatorname{\mathbf {\varepsilon }}_\infty ={1}{255}\)  has a much lower ECE than the standard model: 10.8% instead of 16.6%, and with temperature rescaling 6.7% instead of 11.3%.
